An Approach to Efficient Processing of Multi-word Units

Krstev, Cvetana; Obradović, Ivan; Stanković, Ranka; Vitas, Duško

doi:10.1007/978-3-642-34399-5_6

Cited by 4 publications

(6 citation statements)

References 15 publications

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“…These approaches are extremely precise, but costly. The manual assignment of inflection rules may be eased by tools like Leximir for predicting inflection classes (Krstev et al 2013).…”

Section: Rule-based Methodsmentioning

confidence: 99%

Multiword Expression Processing: A Survey

Constant

Eryiğit

Monti

et al. 2017

Computational Linguistics

148

126

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Multiword expressions (MWEs) are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by “MWE processing,” distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives.

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“…These approaches are extremely precise, but costly. The manual assignment of inflection rules may be eased by tools like Leximir for predicting inflection classes (Krstev et al 2013).…”

Section: Rule-based Methodsmentioning

confidence: 99%

Multiword Expression Processing: A Survey

Constant

Eryiğit

Monti

et al. 2017

Computational Linguistics

148

126

View full text Add to dashboard Cite

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“…There was no a priori knowledge of the syntactic structure of MWTs belonging to this domain. Instead, syntactic patterns of MWEs in Serbian were used based on previous analyses of their structures (Krstev et al , 2013; Stanković et al , 2011; Utvić, 2011). From the terminology perspective, the interest is in nouns (used to name concepts).…”

Section: Acquisition Of Multiword Terms From the Agricultural Domain In Serbianmentioning

confidence: 99%

“…The complete list of syntactic patterns used in the present research is given in Table I. The choice of syntactic patterns was based on previous analyses of the structures of the MWEs (Krstev et al , 2013), already present in Serbian e-dictionaries (that contained some terminology mostly from library and information sciences; see Section 3.6). These analyses revealed that the most frequent nominal MWEs in Serbian have two components (82.9 per cent) followed by MWEs with three components (13.7 per cent), while longer MWEs are much less frequent (3.4 per cent).…”

Section: Acquisition Of Multiword Terms From the Agricultural Domain In Serbianmentioning

confidence: 99%

“…The morphological dictionary of MWEs for Serbian is in DELAC format and has 13,676 entries (Krstev et al , 2013). It contains both general lexica and proper names.…”

Section: Acquisition Of Multiword Terms From the Agricultural Domain In Serbianmentioning

confidence: 99%

“…The concentration is on texts from the agricultural engineering domain and on extracting MWTs from them. Krstev et al (2013) already automated the production of complex dictionary entries in DELAC format for a given list of MWEs, using e-dictionaries of Serbian simple words, inflectional FSTs and a set of grammatical rules. Therefore, the focus is on identifying MWTs in a text, in a manner similar to that proposed by Krstev et al (2014), but modified to suit the patterns explained later.…”

Section: Acquisition Of Multiword Terms From the Agricultural Domain In Serbianmentioning

confidence: 99%

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Semi-automatic extraction of multiword terms from domain-specific corpora

Pajić

Stankovic

Stanković

et al. 2018

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Purpose A hybrid approach is presented, which combines linguistic and statistical information to semi-automatically extract multiword term candidates from texts. Design/methodology/approach The method is designed to be domain and language independent, focusing on languages with rich morphology. Here, it is used for extracting multiword terms from texts in Serbian, belonging to the agricultural engineering domain, as a use case. Predefined syntactic structures were used for multiword terms. For each structure, a finite state transducer was developed, which recognizes text sequences having that structure and outputs the sequence in a normalized form, so that different inflectional forms of the same multiword term can be counted properly. Term candidates were further filtered by their frequencies and evaluated by two domain experts. Findings By using language resources, such as electronic dictionaries and grammars, 928 multiword terms were extracted out of 1,523 multiword terms that were recognized as candidates from a corpus having 42,260 different simple word forms; 870 of these were new, not already contained in the existing electronic dictionary of compounds for Serbian, and they were used to enrich the dictionary. Originality/value The paper presents methodology that can significantly contribute to the development of terminology lexicons in different areas. In this particular use case, some important agricultural engineering concepts were extracted from the text, but this approach could be used for other domains and languages as well.

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